docs: def fit(self, X, y=None, input_type='data'):
          """Fit the model from data in X.
  
          Parameters
          ----------
          input_type : string, one of: 'data', 'distance'.
              The values of input data X. (default = 'data')
          X : array-like, shape (n_samples, n_features)
              Training vector, where n_samples in the number of samples
              and n_features is the number of features.
  
          If self.input_type is 'distance':
  
          X : array-like, shape (n_samples, n_samples),
              Interpret X as precomputed distance or adjacency graph
              computed from samples.
  
          Returns
          -------
          self : object
              Returns the instance itself.
          """
          X = self._validate_input(X, input_type)
          self.fit_geometry(X, input_type)
          random_state = check_random_state(self.random_state)
          self.embedding_, self.error_ = locally_linear_embedding(self.geom_,
                                                                  n_components=self.n_components,
                                                                  eigen_solver=self.eigen_solver,
                                                                  random_state=self.random_state,
                                                                  reg=self.reg,
                                                                  solver_kwds=self.solver_kwds)
          return self